Multi-level stage locality selection on a large system

ABSTRACT

A method for execution by a computing device of a dispersed storage network (DSN). The method begins with obtaining a plurality of write requests. The method continues where for a write request of the plurality of write requests, the computing device generates a vault identification and a generation number. The method continues where the computing device obtains a rounded timestamp and a capacity factor and generates a temporary object number based on the rounded timestamp and the capacity factor. The method continues where the computing device generates a temporary source name based on the vault identification, the generation number, and the temporary object number. The method continues where the computing device identifies a set of storage units of a plurality of sets of storage units of the DSN based on the temporary source name.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent Application claims priority pursuant to35 U. S.C. §120 as a continuation-in-part of U.S. Utility ApplicationSer. No. 14/636,860, entitled “ADJUSTING A NUMBER OF DISPERSED STORAGEUNITS”, filed Mar. 3, 2015, which claims priority pursuant to 35 U.S.C.§119(e) to U.S. Provisional Application No. 61/986,399, entitled“ALLOCATING STORAGE GENERATIONS IN A DISPERSED STORAGE NETWORK”, filedApr. 30, 2014, both of which are hereby incorporated herein by referencein their entirety and made part of the present U.S. Utility PatentApplication for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of an embodiment of a DS clientmodule in accordance with the present invention; and

FIG. 10 is a logic diagram of an example of a method of generating avirtual address in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is FIG. 1 is a schematic block diagram of an embodiment of adispersed, or distributed, storage network (DSN) 10 that includes aplurality of computing devices 12-16, a managing unit 18, an integrityprocessing unit 20, and a DSN memory 22. The components of the DSN 10are coupled to a network 24, which may include one or more wirelessand/or wire lined communication systems; one or more non-public intranetsystems and/or public internet systems; and/or one or more local areanetworks (LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data (e.g., data 40) on behalf of computing device 14. Withthe use of dispersed storage error encoding and decoding, the DSN 10 istolerant of a significant number of storage unit failures (the number offailures is based on parameters of the dispersed storage error encodingfunction) without loss of data and without the need for a redundant orbackup copies of the data. Further, the DSN 10 stores data for anindefinite period of time without data loss and in a secure manner(e.g., the system is very resistant to unauthorized attempts ataccessing the data).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSTN memory 22for a user device, a group of devices, or for public access andestablishes per vault dispersed storage (DS) error encoding parametersfor a vault. The managing unit 18 facilitates storage of DS errorencoding parameters for each vault by updating registry information ofthe DSN 10, where the registry information may be stored in the DSNmemory 22, a computing device 12-16, the managing unit 18, and/or theintegrity processing unit 20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSTN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the I0 deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as I0 ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R)of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of a dispersed storage (DS) clientmodule that includes an ID generation module 90, a first deterministicfunction module 95, a source name generator module 100, a roundingmodule 112, a second deterministic function module 97, a storage unitselection module 106, and a combining module 108. The DS client module34 may be implemented utilizing the DS client module 34 of FIG. 1. TheDS client module 34 functions to generate a source name for a write datarequest.

In an example of operation of generating the source name, the IDgeneration module 90 generates a vault ID 92 and a generation number 94for a received write request 88 for vault A. The generating may includeone or more of performing a registry lookup, accessing a requestingentity to vault ID table, and determining a current generation numberindicator for the vault ID. The rounding module 112 performs a roundingto a current timestamp 110 to produce a rounded timestamp 114. As anexample, the rounding module 112 may perform a rounding to the currenttimestamp 110 up to the end of a nearest time period (e.g., ten minutes)to produce a new rounded timestamp 114. Note the DS client module mayalso utilize the current timestamp to determine to add connections tomore sets of storage units when detecting that a timeframe is about tochange to the next timeframe.

With the rounded timestamp produced, the first deterministic functionmodule 95 obtains a capacity factor 96. The capacity factor 96 includesone or more of an expected processing performance level (e.g.,input-output capacity, processing capacity, performance history, numberof simultaneous write operations, etc.) of the set of storage units(e.g., set of storage units 36) and an expected processing performancelevel of a computing device (e.g., the DS client module 34). Theobtaining includes at least one of determining based on performanceinformation for available sets of storage units, performing a lookup,interpreting an error message, and identifying a capacity level of acomputing device (e.g., the DS client module). For example, the firstdeterministic function module 95 obtains the capacity factor 96 thatindicates that a third set of storage units of a group of ten sets ofstorage units is associated with most favorable levels of expectedprocessing performance. Having obtained the capacity factor 96, thefirst deterministic function module 95 performs a first deterministicfunction (e.g., a hash) on the capacity factor 96 and the roundedtimestamp 114 to produce a temporary object number 98. The temporaryobject number 98 is associated with a desired set of storage units for atime duration associated with the rounded timestamp 114. For example,the first deterministic function module 95 performs the firstdeterministic function to produce the temporary object number 98associated with the third set of storage units (e.g., a best-performingset of storage units).

The source name generator module 100 generates a temporary source name102 that includes the vault ID 92, the generation number 94, and thetemporary object number 98. The storage unit selection module 106identifies the associated set of storage units 36 based on the temporarysource name 102. For example, the storage unit selection module 106accesses a source name to storage unit identifier table utilizing thetemporary source name 102 to produce an identifier 104 of the associatedset of storage units. For instance, the storage unit selection module106 accesses the source name to storage unit identifier table to producea set of storage unit identifiers 104 for the third set of storage units36. Each storage unit 36 of the associated set of storage units 36 isassociated with an address range assignment that includes the temporarysource name 102.

The second deterministic function module 97 applies a seconddeterministic function to the capacity factor 96 and the roundedtimestamp 114 to produce an object number modifier 118, where the objectnumber modifier 118 is to be associated with all data objects writtenwithin a time frame associated with the rounded timestamp 114 inaccordance with the capacity factor 96. The combining module 108combines the temporary source name 102 and the object number modifier118 to produce the source name 120 that includes the vault ID 92, thegeneration number 94, and an object number, where the object number ismodified utilizing the object number modifier 118. For example, thecombining module 108 modifies a middle section of the temporary objectnumber 98 with bits of the object number modifier 118 to provide storagelocality during the time frame associated with the rounded timestamp114. For instance, source names 120 generated during the timeframe shallhave close locality for different associated objects.

Having generated the source name 120, the DS client module 34 generatesa plurality of sets of slice names utilizing the source name 120. Forexample, the DS client module 34 determines entries of a slice indexfield, where a different slice index entry is utilized for each slicename of the set of slice names. As another example, the DS client module34 determines entries of a segment number field as a function of a sizeof the data object for storage. Having generated the plurality of setsof slice names, the DS client module 34 utilizes the plurality of setsof slice names when issuing write slice requests to the set of storageunits associated with the write data request. For example, the DS clientmodule 34 generates a set of write slice requests that includes a set ofslice names and sends the set of write slice requests to the third setof storage units.

FIG. 10 is a flowchart illustrating an example of generating a virtualaddress for storing data. In one example, the generating may be inaccordance with a multi-level stage locality selection protocol. Themulti-level stage locality selection protocol includes identifying a setof storage units and determining a namespace range within the set ofstorage units for which multiple writers will write to during a specifictime period. The method of generating the virtual address begins at step138, where a computing device (e.g., a dispersed storage (DS) clientmodule, a processing module of the DS client module, etc.) obtains aplurality of write requests. The method continues with step 140 wherethe computing device generates a vault identifier (ID) and a generationnumber for a write request of the plurality of write requests. Themethod continues at step 142 where the computing device obtains arounded timestamp and a capacity factor. Note the rounded timestamp maybe generated by rounding a current timestamp to a time that correspondsto the end of a certain time period (e.g., rounding up every minute,every five minutes, every 30 minutes, etc.). The method continues atstep 144 where the computing device generates a temporary object numberbased on the rounded timestamp and the capacity factor. As an example,the computing device performs a first deterministic function on therounded timestamp and the capacity factor to produce the temporaryobject number. Note the temporary object number is associated with apreferred set of storage units (e.g., storage units with favorableperformance, storage units with active connections, etc).

The method continues at step 146 where the computing device generates atemporary source name that includes the vault ID, the generation number,and the temporary object number. The method continues at step 148 wherethe computing device identifies a set of storage units associated withthe temporary source name. For example, the computing device identifiesa third set of storage units (e.g., the preferred set of storage units)based on the temporary source name the method continues at step 150where the computing device generates an object number modifier based onthe rounded time stamp and the capacity factor. For example, thecomputing device performs a second deterministic function on the roundedtimestamp and the capacity factor to produce the object number modifier.As another example, the computing device performs the seconddeterministic function to generate a bit pattern for middle bits of anobject number to provide a desired locality of storage within thepreferred set of storage units.

The method continues at step 152 where the computing device combines thetemporary source name and the object number modifier to produce a sourcename that includes the vault ID, the generation number, and an objectnumber. For example, the computing device overwrites one or more bits ofthe temporary object number with the object number modifier to producethe object number.

The method continues at step 154 where the computing device dispersedstorage error encodes one or more data segments of data of the writerequest to produce one or more sets of encoded data slices. The methodcontinues at step 156 where the computing device generates one or moresets of slice names using the source name, where the one or more sets ofslice names corresponds to the one or more sets of encoded data slices.For example, computing device of appends a slice index and a segmentnumber to the source name for one or more segments of the data. Themethod continues at step 158 where the computing device issues at leastone set of write slice requests to the set of storage units, where theat least one set of write slice requests includes the one or more setsof encoded data slices and the one or more sets of slice names.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a computing device of a dispersed storage network (DSN), the method comprises: obtaining a plurality of write requests; and for a write request of the plurality of write requests: generating a vault identification and a generation number; obtaining a rounded timestamp and a capacity factor; generating a temporary object number based on the rounded timestamp and the capacity factor; generating a temporary source name based on the vault identification, the generation number, and the temporary object number; and identifying a set of storage units of a plurality of sets of storage units of the DSN based on the temporary source name.
 2. The method of claim 1, wherein the temporary object number is generated by performing a first deterministic function on the rounded timestamp and the capacity factor.
 3. The method of claim 1 further comprises: generating an object number modifier based on the rounded timestamp and the capacity factor; combining the temporary source name and the object number modifier to produce a source name, wherein the source name includes the vault identification, the generation number and an object number, wherein the temporary object number is modified based on the object number modifier to produce the object number; dispersed storage error encoding one or more data segments of the write request to produce one or more sets of encoded data slices; generating one or more sets of slice names using the source name, wherein the one or more sets of slice names correspond to the one or more sets of encoded data slices; and issuing at least one set of write slice requests to the set of storage units, wherein the at least one set of write slice requests includes the one or more sets of encoded data slices and the one or more sets of slice names.
 4. The method of claim 3, wherein the object number modifier is generated by performing a second deterministic function on the rounded timestamp and the capacity factor.
 5. The method of claim 1, wherein the capacity factor includes at least one of: an expected processing performance level of a set of storage units of the plurality of sets of storage units of the DSN; and an expected processing performance level of the computing device.
 6. The method of claim 1, wherein the obtaining the capacity factor includes at least one of: determining the capacity factor based on performance information for available sets of storage units; performing a lookup; interpreting an error message; and identifying a capacity level of the computing device.
 7. The method of claim 1, wherein the obtaining the rounded timestamp comprises one of: during a time period, generating the rounded timestamp by rounding a current timestamp up to an end of the time period; and receiving the rounded timestamp.
 8. The method of claim 1, wherein the generating the vault identification and the generation number includes one or more of: performing a registry lookup; accessing a requesting entity to vault identification (ID) table; and determining a current generation number indicator for the vault ID.
 9. A computing device of a dispersed storage network (DSN) comprises: memory; an interface; and a processing module operably coupled to the memory and the interface, wherein the processing module is operable to: obtain a plurality of write requests; and for a write request of the plurality of write requests: generate a vault identification and a generation number; obtain a rounded timestamp and a capacity factor; generate a temporary object number based on the rounded timestamp and the capacity factor; generate a temporary source name based on the vault identification, the generation number, and the temporary object number; and identify a set of storage units of a plurality of sets of storage units of the DSN based on the temporary source name.
 10. The computing device of claim 9, wherein the processing module is operable to generate the temporary object number by performing a first deterministic function on the rounded timestamp and the capacity factor.
 11. The computing device of claim 9, wherein the processing module is further operable to: generate an object number modifier based on the rounded timestamp and the capacity factor; combine the temporary source name and the object number modifier to produce a source name, wherein the source name includes the vault identification, the generation number and an object number, wherein the temporary object number is modified based on the object number modifier to produce the object number; dispersed storage error encode data of the write request to produce one or more sets of encoded data slices; generate one or more sets of slice names using the source name, wherein the one or more sets of slice names correspond to the one or more sets of encoded data slices; and issue at least one set of write slice requests to the set of storage units, wherein the at least one set of write slice requests includes the one or more sets of encoded data slices and the one or more sets of slice names.
 12. The computing device of claim 11, wherein the processing module is further operable to generate the object number modifier by performing a second deterministic function on the rounded timestamp and the capacity factor.
 13. The computing device of claim 9, wherein the capacity factor includes at least one of: an expected processing performance level of a set of storage units of the plurality of sets of storage units of the DSN; and an expected processing performance level of the computing device.
 14. The computing device of claim 9, wherein the processing module is operable to obtain the capacity factor by at least one of: determining the capacity factor based on performance information for available sets of storage units; performing a lookup; interpreting an error message; and identifying a capacity level of the computing device.
 15. The computing device of claim 9, wherein processing module is operable to obtain the rounded timestamp by: during a time period, generating the rounded timestamp by rounding a current timestamp up to an end of the time period; and receiving the rounded timestamp.
 16. The computing device of claim 9, wherein the processing module is operable to generate the vault identification and the generation number by one or more of: performing a registry lookup; accessing a requesting entity to vault identification (ID) table; and determining a current generation number indicator for the vault ID. 